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, OpenFOAM), and plasma physics (XGC, IPPL). Expected qualifications: A Master's degree in Computer Science or Applied Mathematics. Necessary knowledge: Modern C++, GPU computing with CUDA/SYCL, MPI, Krylov
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Ph.D. in applied mathematics, computer science, or computational science/engineering 2+ years experience beyond Ph.D . in an applied mathematics, computer science, or computational science/engineering
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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Empire Innovation Professor (Associate Professor) Artificial Intelligence in Biomolecular and Drug D
Empire Innovation Professor (Associate Professor) Artificial Intelligence in Biomolecular and Drug Design, Applied Mathematics and Statistics - College of Engineering and Applied Sciences Location
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, applied mathematics, or related fields. The ideal candidate should be organised, highly motivated, creative, and interested to work as part as a collective. A strong track record of first-author
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, Computational Science, Applied Mathematics, or a related field. Soft skills Critical thinking and problem-solving, Collaboration and teamwork, Good communication, Time management and self-motivation Hard skills
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support for DNN loads. What we are looking for: The successful candidate: Will have a PhD (or be very near completion or equivalent) in engineering, mathematics, physics, or a related topic. Experience with
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Innovation Professor (Associate Professor) Artificial Intelligence in Biomolecular and Drug Design, Applied Mathematics and Statistics - College of Engineering and Applied Sciences Location: Department
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of parallel computing (GPUs) to speed solution within the optimisation process. Funding Notes 1st or 2:1 degree in Engineering, Materials Science, Physics, Chemistry, Applied Mathematics, or other Relevant
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-impact journals and conferences. Mentor students in related research projects. Qualifications : A Ph.D. (awarded or near completion) in applied mathematics, computer science, or a closely related field